It is shown that the sum of the sample autocorrelation function at lag h≥1 is always for any stationary time series with arbitrary length T≥2 (Hassani, 2009 [1]). In this paper, the distribution of a set of the sample autocorrelation function using the properties of this quantity is considered. It is found that the distribution of a set of the sample autocorrelation estimates is not independent and identically distributed. This finding implies that the result of diagnostic check and model building using the traditional assumption of iid can be quite misleading
We explain the connection between autocorrelation functions of stationary continuous time processes ...
In the first part of the study, nine estimators of the first-order autoregressive parameter are revi...
Time series generated by Stochastic Volatility (SV) processes are uncorrelated although not independ...
It is shown that the sum of the sample autocorrelation function at lag h≥1 is always for any statio...
The behaviour of the sample autocorrelation coefficients is important for the identification of the ...
The detection of long-range dependence in time series analysis is an important task to which this pa...
The detection of long-range dependence in time series analysis is an important task to which this pa...
Sample autocorrelation coefficients are widely used to test the randomness of a time series. Despite...
When studying a real-life time series, it is frequently reasonable to assume, possibly after a suita...
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
. For the stable moving average process X t = Z 1 \Gamma1 f(t + x)M(dx); t = 1; 2; ::: we find ...
The subject of the thesis is the autocorrelation structure of time series. AR(1) process is studied ...
We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals...
AbstractWe consider a stationary time series {Xt} given by Xt = ΣkψkZt − k, where the driving stream...
In second-order statistical inference, an interval estimate of autocorrelation is a convenient repor...
We explain the connection between autocorrelation functions of stationary continuous time processes ...
In the first part of the study, nine estimators of the first-order autoregressive parameter are revi...
Time series generated by Stochastic Volatility (SV) processes are uncorrelated although not independ...
It is shown that the sum of the sample autocorrelation function at lag h≥1 is always for any statio...
The behaviour of the sample autocorrelation coefficients is important for the identification of the ...
The detection of long-range dependence in time series analysis is an important task to which this pa...
The detection of long-range dependence in time series analysis is an important task to which this pa...
Sample autocorrelation coefficients are widely used to test the randomness of a time series. Despite...
When studying a real-life time series, it is frequently reasonable to assume, possibly after a suita...
The sample autocorrelation function is defined by the mean lagged products (LPs) of random observati...
. For the stable moving average process X t = Z 1 \Gamma1 f(t + x)M(dx); t = 1; 2; ::: we find ...
The subject of the thesis is the autocorrelation structure of time series. AR(1) process is studied ...
We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals...
AbstractWe consider a stationary time series {Xt} given by Xt = ΣkψkZt − k, where the driving stream...
In second-order statistical inference, an interval estimate of autocorrelation is a convenient repor...
We explain the connection between autocorrelation functions of stationary continuous time processes ...
In the first part of the study, nine estimators of the first-order autoregressive parameter are revi...
Time series generated by Stochastic Volatility (SV) processes are uncorrelated although not independ...